A
Discussion
with
the
FIFRA
Scientific
Advisory
Panel
Regarding
the
Terrestrial
and
Aquatic
Level
II
Refined
Risk
Assessment
Models
(
Version
2.0)

Meeting
Scheduled
for
March
30
­
April
2,
2004
March
4,
2004
Environmental
Fate
and
Effects
Division
Office
of
Pesticide
Programs
U.
S.
Environmental
Protection
Agency
1Scientists
identified
are
in
the
Environmental
Fate
and
Effects
Division,
Office
of
Pesticide
Programs
unless
noted
otherwise.

Page
ii
of
x
Acknowledgements1
Authors:
Refined
Risk
Assessment
Implementation
Team
Chair:
Ingrid
Sunzenauer
Terrestrial
Team:
Edward
Fite
(
Lead)
Timothy
Barry,
Office
of
Economy
and
Environment
Henry
Nelson,
Office
of
Science
Policy
and
Coordination
Edward
Odenkirchen
Dirk
Young
Aquatic
Team:
Donna
Randall
(
Lead)
Timothy
Barry,
Office
of
Economy
and
Environment
Marietta
Echeverria
Dirk
Young
Additional
Supporting
Team
Members:
Douglas
Urban
(
Deceased)
Stephanie
Irene
Additional
Contributors:
Christine
Hartless
James
Carleton
Reviewers:
Steven
Bradbury
R.
David
Jones
James
Hetrick
Karen
McCormack
Page
iii
of
x
Table
of
Contents
Executive
Summary
Chapter
I.
Introduction
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1­
1
A.
Purpose
and
Organization
of
Document
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1­
1
B.
History
and
Goals
of
Initiative
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1­
1
C.
Overview
of
Proposed
Conceptual
Risk
Assessment
Process
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1­
3
1.
Levels
of
Refinement
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1­
3
2.
Guidance
to
Move
Between
the
Levels
of
Refinement
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1­
4
Chapter
II.
Charge
to
the
Panel
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2­
1
A.
Purpose
of
This
Consultation
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2­
1
B.
Questions
Regarding
the
Terrestrial
Level
II
Model
(
Version
2.0)
.
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2­
1
C.
Questions
Regarding
the
Aquatic
Level
II
Model
(
Version
2.0)
.
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2­
4
Chapter
III.
Terrestrial
Level
II
Model
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3­
1
A.
Introduction
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3­
1
B.
General
Model
Overview
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3­
2
C.
Generic
Species
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3­
5
1.
Background
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3­
5
2.
Generic
Species
Approach
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3­
6
D.
Bimodal
Feeding
Model
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3­
9
1.
Modeling
Approach
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3­
10
2.
Markov
Chain
Model
of
On­
Field
Avian
Persistence
.
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3­
16
3.
Incorporation
of
the
Markov
Chain
Model
into
the
Level
II
Algorithm
for
Avian
On­
field
persistence.
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3­
17
E.
Puddle
Model
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3­
24
1.
Background
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3­
24
2.
Model
Description
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3­
24
a.
Field
Hydrology
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3­
24
b.
Puddle
Hydrodynamics
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3­
25
c.
Field
Contaminant
Hydrology
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3­
26
d.
Puddle
Contaminant
Hydrodynamics
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3­
29
3.
Summary
of
Puddle
Model
Intervals
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3­
31
4.
Parameterization
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3­
32
5.
Future
Developments
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3­
35
F.
Model
for
Inhalation
Exposure
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3­
38
1.
General
Inhalation
Exposure
Model
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3­
38
2.
Consideration
of
Other
Agency
Approaches
in
Modeling
the
Air
Concentration
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3­
40
3.
Combining
the
HWIR
Approach
with
Mass
Conservation
in
a
Two­
Compartment
Equilibrium
Model
.
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3­
41
4.
Parameterization
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3­
43
Page
iv
of
x
5.
Scenarios
for
Consideration
of
Pesticide
Exposure
Through
Inhalation
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3­
44
6.
Relating
External
Inhalation
Dose
to
Oral
Dose
Equivalents
.
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3­
44
7.
Next
Steps
for
Inhalation
Exposure
Model
.
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3­
45
a.
Examination
and
Comparison
of
Alternative
Air
Concentration
Models
with
Available
Air
Measurement
Data
.
.
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3­
45
b.
Sensitivity
Analysis
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3­
46
G.
Model
for
Dermal
Exposure
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3­
46
1.
General
Dermal
Exposure
Model
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3­
46
2.
Transfer
Coefficient
(
TC)
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3­
50
3.
Parameterization
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3­
50
4.
Scenarios
for
Consideration
of
Pesticide
Exposure
Through
Dermal
Exposure
.
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3­
51
5.
Relating
External
Dermal
Dose
to
Oral
Dose
Equivalents
.
.
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3­
52
6.
Sensitivity
Analysis
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3­
53
Chapter
IV.
The
Aquatic
Level
II
Refined
Risk
Assessment
Model
(
Version
2.0)
.
.
.
.
.
.
.
.
4­
1
A.
Introduction
.
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4­
1
B.
Overview
of
the
Aquatic
Level
II
Assessment
Objectives
and
Model
.
.
.
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.
.
4­
2
1.
Objectives
.
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4­
2
2.
Summary
of
Aquatic
Level
II
RRA
Model
.
.
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4­
2
3.
Software
Package
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4­
7
C.
A
Varying
Volume
Water
Body
Model
with
Daily
Parameter
Variations
for
Pesticide
Risk
Assessments
.
.
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4­
9
1.
Introduction
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4­
9
2.
Current
Ecological
Surface
Water
Scenario
and
Model
.
.
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.
4­
10
3.
Varying
Volume
Surface
Water
Body
Model
(
VVWM)
.
.
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.
4­
10
a.
Solute
Holding
Capacity
Ratio
(
 )
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.
4­
18
b.
Effective
Littoral
Zone
Dissipation
(
 
1)
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4­
20
3.
Effective
Benthic
Zone
Dissipation
(
 
2)
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
4­
32
a.
Benthic
hydrolysis
(

hydr_
2)
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
4­
32
b.
Benthic
Metabolism
(

bio_
2)
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
4­
35
4.
Mass
Transfer
Coefficient
(
 
)
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
4­
35
5.
Daily
Piecewise
Calculations
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
4­
38
a.
Volume
Calculations
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
4­
38
6.
Daily
Initial
Pesticide
Conditions
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
4­
38
7.
Analytical
Solution
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
4­
39
8.
Testing
and
Comparison
of
Solution
with
EXAMS
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
4­
41
9.
Summary
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
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.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
4­
41
D.
Testing
and
Evaluation
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
4­
45
1.
Introduction
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
4­
45
2.
Methods
and
Approaches
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
4­
45
a.
QA/
QC
Testing
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
4­
45
Page
v
of
x
b.
Evaluation
of
the
RRA
Level
II
Exposure
Model
.
.
.
.
.
.
.
.
.
.
.
.
4­
47
3.
Results
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
4­
49
a.
QA/
QC
Testing
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
4­
49
b.
Evaluation
of
the
RRA
Level
II
Exposure
Model
.
.
.
.
.
.
.
.
.
.
.
.
4­
54
4.
Summary
and
Conclusions
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
4­
55
E.
Development
and
Evaluation
of
Scenario­
Specific
Field
Drainage
Area
and
Water
Body
Size
Parameter
Conditions
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
4­
63
1.
Introduction
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
4­
63
2.
The
Current
Standard
Surface
Water
Ecological
Exposure
Modeling
Scenario
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
4­
63
3.
Sources
of
DA/
VC
Ratios
and
Associated
Surface
Water
Body
Dimensions
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
4­
67
4.
Development
and
Evaluation
of
Field
Drainage
Area
and
Water
Body
Size
Conditions
for
Crop
Scenarios
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
4­
68
a.
Methods
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
4­
68
b.
Results
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
4­
69
5.
Proposed
Crop
Scenario­
Specific
Field
Drainage
Area
and
Water
Body
Size
Parameter
Values
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
4­
80
6.
Summary
and
Conclusions
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
4­
91
F.
Implementation
of
a
Probabilistic
Curve
Number
Method
in
the
PRZM
Runoff
Model
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
4­
93
1.
Introduction
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
4­
93
2.
Background
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
4­
93
3.
Probabilistic
Treatments
of
Curve
Number
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
4­
97
4.
Continuous
Simulation
Modeling
With
the
Curve
Number
.
.
.
.
.
.
.
.
.
.
.
4­
99
5.
The
PRZM
3.12
Method
of
Curve
Number
Implementation
.
.
.
.
.
.
.
.
.
.
4­
100
a.
Incorporation
of
a
Probabilistic
Curve
Number
into
PRZM
.
.
.
.
.
4­
101
b.
Example
Implementation
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
4­
105
6.
Summary
and
Conclusions
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
4­
106
Chapter
V.
Next
Steps
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
5­
1
A.
Finalizing
the
Level
II
Models
(
Version
2.0)
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
5­
1
B.
Training
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
5­
1
1.
Risk
Assessors
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
5­
2
2.
Risk
Managers
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
5­
2
3.
Risk
Assessors
and
Risk
Managers
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
5­
2
C.
Outreach
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
5­
2
D.
Building
Partnerships
and
Future
Directions
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
5­
3
1.
Addressing
Uncertainties
Related
to
Effects
Characterization
.
.
.
.
.
.
.
.
.
.
.
5­
3
a.
Extrapolation
Research
for
Predicting
Toxicological
Responses
.
.
.
5­
3
b.
Advancing
Techniques
for
Assessing
Risk
of
Pesticides
.
.
.
.
.
.
.
.
.
5­
4
2.
Addressing
Exposure
Uncertainties
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
5­
4
a.
Database
of
Regional
Small
Surface
Water
Body
Characteristics
.
.
5­
5
Page
vi
of
x
b.
Initial
Application
Date
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
5­
5
c.
Development
of
Uncertainty
and
Extrapolation
Factors
for
Metabolism
Rate
Constants
for
Use
in
Modeling
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
5­
5
3.
Predictions
of
Population
Dynamics
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
5­
6
Chapter
VI.
Bibliography
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
6­
1
Page
vii
of
x
Table
and
Figures
Table
3­
1.
Generic
Species
for
Level
II
Assessments
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
3­
9
Table
3­
2.
Transition
probabilities
for
various
combinations
of
the
long­
run,
on­
field
probability,

 
1,
and
Q,
the
bias
factor.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
3­
23
Table
3­
3.
List
of
input
parameters
for
puddle
model.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
3­
33
Table
3­
4.
A
Summary
of
Mixing
Zone
Depths
from
the
Literature
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
3­
35
Table
3­
5.
Inhalation
model
parameters.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
3­
43
Table
3­
6.
Dermal
model
parameters
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
3­
51
Table
4­
1.
Level
I
and
II
Aquatic
Risk
Assessment
Exposure
Components
.
.
.
.
.
.
.
.
.
.
.
.
.
4­
6
Table
4­
2.
Level
I
and
II
Aquatic
Risk
Assessment
Acute
Effects
Components
.
.
.
.
.
.
.
.
.
.
4­
8
Table
4­
3.
Parameters
and
standard
values
used
in
the
current
EXAMS­
based
model.
Parameters
are
defined
in
the
EXAMS
user
manual
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
4­
11
Table
4­
4.
Parameter
values
for
the
current
EXAMS­
based
model
in
terms
of
the
VVWM
definitions
of
the
parameters.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
4­
16
Table
4­
5.
VVWM
parameter
equivalents
to
EXAMS
parameters(
a)
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
4­
17
Table
4­
6.
Input
Parameters
for
Short­
lived
Chemical,
ChemA,
and
Long­
lived
chemical,
ChemB
4­
48
Table
4­
7.
Summary
of
PRZM/
EXAMS
and
PRZM/
VVWM
Field
Area
and
Water
Body
Size
Variables
and
Their
Values
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
4­
48
Table
4­
8.
Identified
Disparities
Between
the
PE4
Shell
and
the
RRA
Shell
for
Generating
PRZM
Input
Files
and
Any
Subsequent
Modifications
Made
to
Address
Issues
.
.
.
.
.
.
.
.
.
4­
50
Table
4­
9.
Identified
Incompatibilities
of
Standard
Crop
Scenarios
and
Corresponding
Meteorological
Files
with
RRA
Shell
and
Subsequent
Modifications
Made
to
Address
Issues
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
4­
51
Table
4­
10.
Identified
Disparities
Between
VVWM
and
EXAMS
Static
Water
Body
Model
While
Holding
the
Volume
of
the
Water
Body
in
VVWM
constant
and
Equal
to
the
Water
Body
Volume
in
EXAMS
(
20,000
m3)
and
Subsequent
Modification
Made
to
Address
Issues
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
4­
52
Table
4­
11.
Water
body
volume
for
a
given
depth
and
set
surface
area
of
1
ha
(
10,000
m2)
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
4­
70
Table
4­
12.
Field
drainage
area
(
ha)
for
a
given
water
body
depth
and
water
body
volume.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
4­
70
Table
4­
13.
Water
body
surface
area
for
a
given
initial
depth
and
set
water
body
volume
(
20,000
m3).
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
4­
71
Table
4­
14.
Field
drainage
area
for
a
given
depth
and
set
water
body
volume
(
20000
m3).
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
4­
71
Table
4­
15.
Field
drainage
area
and
water
body
size
parameter
values
for
option
(
1)
setting
surface
area,
SA,
to
1
ha,
(
2)
setting
volume
capacity,
VC,
to
20,000
m3,
and
(
3)
setting
field
area,
DA,
to
10,
20,
40,
and
100
ha
for
crop
scenarios
CA
fruit
and
FL
sugarcane.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
4­
73
Page
viii
of
x
Table
4­
16.
Field
area
and
water
body
size
parameter
values
derived
by
setting
field
area
to
10
ha
and
setting
initial
and
maximum
depth
to
the
minimum,
average
and
maximum
of
the
range
in
Figure
4­
20b
for
crop
scenarios
CA
fruit
and
FL
sugarcane.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
4­
73
Table
4­
17.
Proposed
field
and
water
body
size
parameter
values
for
all
standard
crop
scenarios
for
the
Level
II
Exposure
Model.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
4­
81
Table
4­
18.
Summary
of
simulated
water
volume
and
depth
conditions
and
runoff
volume
associated
with
proposed
crop
scenario­
specific
conditions.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
4­
88
Table
4­
19.
Runoff
Curve
Numbers
for
soil
cover
complexes
and
soil
groups
(
Antecedent
Runoff
Condition
II
and
I
a
=
0.2)
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
4­
95
Table
4­
20.
Relationship
of
CN
I
CN
II
and
CN
III
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
4­
96
Figure
3­
1.
Conceptual
Model
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
3­
4
Figure
3­
2.
Hypothetical
examples
of
the
avian
bimodal
feeding
pattern.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
3­
13
Figure
3­
3.
Examples
of
the
betapert
density
used
in
the
bimodal
feeding
pattern
model
and
the
range
of
shapes
that
it
can
assume.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
3­
14
Figure
3­
4.
Examples
of
feeding
fractions
based
on
bimodal
feeding
model.
.
.
.
.
.
.
.
.
.
.
.
3­
15
Figure
3­
5.
The
two­
state,
first­
order
Markov
chain
model
for
avian
persistence.
.
.
.
.
.
.
3­
16
Figure
3­
6.
Region
of
valid
on­
field
to
on­
field
transitional
probability
as
a
function
of
long­
run,
on­
field
probability,
 
1
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
3­
19
Figure
3­
7.
Effect
of
Q
on
the
shape
of
the
triangle
distribution
of
P
11.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
3­
20
Figure
3­
8.
Change
in
avian
on­
field
persistence
(
persistence)
as
it
depends
on
long­
term
on­
field
probability
and
on
Q.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
3­
21
Figure
3­
9.
Figures
(
a),
(
b),
and
(
c)
above
illustrate
the
main
features
of
the
Level
II
bimodal
feeding
model.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
3­
22
Figure
3­
10.
Depiction
of
the
area
that
contributes
to
runoff
to
the
puddle.
.
.
.
.
.
.
.
.
.
.
.
.
3­
36
Figure
3­
11.
Depiction
of
the
hydrologic
processes
controlling
puddle
volume
.
.
.
.
.
.
.
.
.
3­
37
Figure
3­
12.
Depiction
of
the
field
mixing
zone
concept.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
3­
37
Figure
4­
1.
Flowchart
of
the
Level
II
Two­
Dimensional
Monte
Carlo
Risk
Assessment
Model
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
4­
3
Figure
4­
2.
Conceptualization
of
the
Varying
Volume
Water
Body
Model
.
.
.
.
.
.
.
.
.
.
.
.
4­
14
Figure
4­
3.
Solute
holding
capacity
as
a
function
of
K
oc
for
the
current
(
EXAMS­
based)
model.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
4­
21
Figure
4­
4.
Relative
solute
holding
capacity
of
individual
components
in
the
littoral
zone.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
4­
22
Figure
4­
5.
Relative
solute
holding
capacity
of
individual
components
in
benthic
region
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
4­
23
Figure
4­
6.
Effective
half­
life
of
pesticide
due
to
washout
in
a
water
body
as
currently
parameterized
(
1
ha
area
by
2
m
deep).
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
4­
25
Figure
4­
7.
Effect
of
Henry's
Law
Coefficient
and
wind
speed
(
measured
at
6m)
on
effective
volatilization
half­
life
of
aqueous
phase.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
4­
33
Page
ix
of
x
Figure
4­
8.
Effect
of
Henry's
Law
Coefficient
and
temperature
on
effective
volatilization
half­
life
of
aqueous
phase
for
the
current
(
EXAMS­
based)
model.
The
lack
of
temperature
sensitivity
is
a
result
of
not
considering
the
effect
of
temperature
on
Henry's
Law
Coefficient.
Wind
speed
=
1
m/
s,
MW=
100.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
4­
34
Figure
4­
9.
Comparison
the
volatilization
mechanisms
of
the
proposed
VVWM
and
EXAMS
for
conditions
of
pesticide
solubility
=
100
mg/
L,
MW
=
100,
vapor
pressure
=
0.1
torr,
K
oc
=
1
mL/
g,
wind
speed
=
1
m/
s,
temperature
=
25

C,
and
an
input
mass
of
0.02
kg
to
the
littoral
zone.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
4­
42
Figure
4­
10.
Comparison
of
proposed
VVWM
with
EXAMS
for
the
conditions
of
MW
=
100,
solubility
=
100
mg/
L,
vapor
pressure
of
0.01
torr,
aerobic
half­
life
of
10
days,
anaerobic
half­
life
of
100
days,
K
oc
of
100
mL/
g,
wind
speed
of
1
m/
s,
temperature
of
25

C,
and
arbitrarily
selected
PRZM
input
fluxes.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
4­
43
Figure
4­
11.
Flow
chart
of
Level
II
exposure
modeling
approach
compared
to
Level
I
.
.
.
.
4­
46
Figure
4­
12.
RRA
shell
launched
PRZM
output
compared
to
PE4
shell
launched
PRZM
output,
(
a)
mass
loading
in
runoff
(
RFLX)
and
(
b)
mass
loading
in
erosion
(
EFLX).
.
.
.
.
.
.
4­
53
Figure
4­
13.
RRA
Level
II
Exposure
model
(
PRZM/
VVWM)
output
compared
to
PRZM/
EXAMS
exposure
model
where
all
transformation
processes
were
set
to
zero
except
aerobic
metabolism,
which
was
set
to
an
80
day
half­
life.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
4­
56
Figure
4­
14.
RRA
Level
II
Exposure
model
(
PRZM/
VVWM)
output
compared
to
PRZM/
EXAMS
exposure
model
where
all
transformation
processes
were
set
to
zero
except
benthic
metabolism,
which
was
set
to
an
80
day
half­
life.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
4­
57
Figure
4­
15.
RRA
Level
II
Exposure
model
(
PRZM/
VVWM)
output
compared
to
PRZM/
EXAMS
exposure
model
where
all
transformation
processes
were
set
to
zero
except
hydrolysis,
which
was
set
to
an
80
day
half­
life.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
4­
58
Figure
4­
16.
RRA
Level
II
Exposure
model
(
PRZM/
VVWM)
output
compared
to
PRZM/
EXAMS
exposure
model
where
all
transformation
processes
were
set
to
zero
except
photolysis,
which
was
set
to
an
80
day
half­
life.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
4­
59
Figure
4­
17.
RRA
Level
II
Exposure
model
(
PRZM/
VVWM)
output
compared
to
PRZM/
EXAMS
exposure
model
where
all
transformation
processes
were
set
to
zero
except
volatilization,
vapor
pressure
was
set
to
1e­
4
torr.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
4­
60
Figure
4­
18.
EXAMS
and
VVWM
simulated
daily
concentrations
for
(
a)
short­
lived
ChemA
and
(
b)
long­
lived
ChemB
and
water
body
volume
for
CA
almond
crop
scenario.
.
.
.
.
.
4­
61
Figure
4­
19.
EXAMS
and
VVWM
simulated
daily
concentrations
for
(
a)
short­
lived
ChemA
and
(
b)
long­
lived
ChemB
and
water
body
volume
for
FL
sugarcane
crop
scenario.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
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.
.
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.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
4­
62
Figure
4­
20.
(
a)
USDA
recommended
minimum
depth
of
water
for
a
small,
permanent
surface
water
supply
(
e.
g.,
pond)
in
the
U.
S.
(
USDA,
1997).
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
4­
65
Figure
4­
21.
(
a)
USDA
guidelines
for
estimating
the
size
of
drainage
area
(
in
acres)
required
for
each
acre­
foot
of
storage
in
an
embankment
or
pond
(
USDA,
1997).
.
.
.
.
.
.
.
.
.
.
4­
66
Figure
4­
22.
The
effect
of
setting
water
body
surface
area,
initial
volume
or
drainage
area
on
(
a)
daily
concentration
and
(
b)
water
body
volume
for
ChemB
at
a
semi­
arid
site
corresponding
to
the
CA
fruit
crop
scenario
(
metfile
W93193,
DA/
VC
=
50
acre/
acre­
ft,
D
0
=
D
max
=
2.4
m).
.
.
.
.
.
.
.
.
.
.
.
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.
.
.
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.
.
.
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.
.
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.
.
.
.
.
.
.
.
.
.
.
.
.
.
4­
74
Page
x
of
x
Figure
4­
23.
The
effect
of
varying
drainage
areas
on
(
a)
daily
concentration
and
(
b)
water
body
volume
for
ChemB
in
semi­
arid
site
corresponding
to
standard
scenario
CA
fruit
(
metfile
W93193,
DA/
VC
ratio
=
50
acre/
acre­
ft,
and
D
0
=
D
max
=
2.4
m.
.
.
.
.
.
.
.
.
.
.
.
.
.
4­
75
Figure
4­
24.
The
effect
of
using
the
minimum
(
min),
average
(
avg),
and
maximum
(
max)
initial
water
body
depth
(
D0)
on
daily
concentration
for
(
a)
ChemA
and
(
b)
ChemB
and
(
c)
water
body
volume
in
semi­
arid
site
corresponding
to
standard
scenario
CA
fruit,
metfile
w93193.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
4­
78
Figure
4­
25.
The
effect
of
using
the
minimum
(
min),
average
(
avg),
and
maximum
(
max)
initial
water
body
depth
(
D0)
on
daily
concentration
for
(
a)
ChemA
and
(
b)
ChemB
and
(
c)
water
body
volume
in
humid
site
corresponding
to
standard
scenario
FL
sugarcane,
metfile
w12844.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
4­
79
Figure
4­
26.
Comparison
of
VVWM
using
standard
water
body
conditions
and
VVWM
using
crop
scenario­
specific
water
body
conditions
for
(
a)
ChemA,
(
b)
ChemB,
and
(
c)
volume
for
CA
fruit
scenario,
met
w93193.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
4­
84
Figure
4­
27.
Comparison
of
VVWM
using
the
standard
water
body
conditions,
and
VVWM
using
the
crop
scenario­
specific
water
body
conditions
for
(
a)
ChemA,
(
b)
ChemB
and
(
c)
volume
for
FL
sugarcane.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
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.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
4­
86
Figure
4­
28.
Plot
of
the
curve
numbers
that
represent
the
10th
and
90th
percentile
exceedence
frequencies
for
14
watersheds
as
presented
by
Hjelmfelt
(
1991).
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
4­
98
Figure
4­
29.
PRZM's
relationship
of
curve
number
to
soil
moisture.
FC
is
field
capacity,
and
WP
is
wilting
point.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
4­
102
Figure
4­
30.
Curve
numbers
implied
by
the
data
of
Wauchope
et
al.
(
1999)
plotted
as
a
function
of
soil
moisture.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
4­
103
Figure
4­
31.
A
comparison
of
curve
numbers
derived
from
field
data
with
those
generated
by
the
probabilistic
curve
number
and
those
generated
by
PRZM.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
4­
107
Figure
4­
32.
Curve
numbers
generated
probabilistically
and
by
PRZM
shown
as
a
function
of
soil
moisture.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
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.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
4­
108
Figure
4­
33.
A
depiction
of
the
runoff/
rainfall
relationship
of
the
measured
field
data
and
PRZM
3.12
simulated
values.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
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.
.
.
.
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.
.
.
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.
.
.
.
.
.
.
.
4­
109
